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app.py
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app.py
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from crypt import methods
import time
from django.shortcuts import render
from flask import Flask, render_template , request
from runfile import colorize , colorize_video
import re
from django.shortcuts import render
import os
from runfile import colorize
#DL imports
import pickle
from pandas_datareader import test
from tqdm.notebook import tqdm
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.utils import to_categorical, plot_model
import numpy as np
from keras.models import load_model
app = Flask(__name__)
@app.route('/home')
def index():
source_url = 'https://images.unsplash.com/photo-1578393098337-5594cce112da?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=617&q=80'
colorize(source_url)
return render_template('demo.html')
# Create your views here.
MODEL_DIR = 'models'
# create data generator to get data in batch (avoids session crash)
def data_generator(data_keys, mapping, features, tokenizer, max_length, vocab_size, batch_size):
# loop over images
X1, X2, y = list(), list(), list()
n = 0
while 1:
for key in data_keys:
n += 1
captions = mapping[key]
# process each caption
for caption in captions:
# encode the sequence
seq = tokenizer.texts_to_sequences([caption])[0]
# split the sequence into X, y pairs
for i in range(1, len(seq)):
# split into input and output pairs
in_seq, out_seq = seq[:i], seq[i]
# pad input sequence
in_seq = pad_sequences([in_seq], maxlen=max_length)[0]
# encode output sequence
out_seq = to_categorical([out_seq], num_classes=vocab_size)[0]
# store the sequences
X1.append(features[key][0])
X2.append(in_seq)
y.append(out_seq)
if n == batch_size:
X1, X2, y = np.array(X1), np.array(X2), np.array(y)
yield [X1, X2], y
X1, X2, y = list(), list(), list()
n = 0
def idx_to_word(integer, tokenizer):
for word, index in tokenizer.word_index.items():
if index == integer:
return word
return None
def predict_caption(model, image, tokenizer, max_length):
in_text = 'startseq'
for i in range(max_length):
sequence = tokenizer.texts_to_sequences([in_text])[0]
sequence = pad_sequences([sequence], max_length)
yhat = model.predict([image, sequence], verbose=0)
yhat = np.argmax(yhat)
word = idx_to_word(yhat, tokenizer)
if word is None:
break
in_text += " " + word
if word == 'endseq':
break
return in_text
def clean(mapping):
for key, captions in mapping.items():
for i in range(len(captions)):
# take one caption at a time
caption = captions[i]
# preprocessing steps
# convert to lowercase
caption = caption.lower()
# delete digits, special chars, etc.,
caption = caption.replace('[^A-Za-z]', '')
# delete additional spaces
caption = caption.replace('\s+', ' ')
# add start and end tags to the caption
caption = 'startseq ' + " ".join([word for word in caption.split() if len(word)>1]) + ' endseq'
captions[i] = caption
@app.route('/', methods =["GET", "POST"])
def home():
if request.method == 'POST':
text = request.form.get('arttext')
img_name = request.form.get('captionimg')
print(type(img_name))
print(img_name)
if img_name:
with open('models/features.pkl', 'rb') as f:
# with open(os.path.join(MODEL_DIR +'/features.pkl'), 'rb') as f:
features = pickle.load(f)
print("Features loaded")
with open('models/captions.txt', 'r') as f:
next(f)
captions_doc = f.read()
print("Captions loaded")
# create mapping of image to captions
mapping = {}
for line in tqdm(captions_doc.split('\n')):
tokens = line.split(',')
if len(line) < 2:
continue
image_id, caption = tokens[0], tokens[1:]
image_id = image_id.split('.')[0]
caption = " ".join(caption)
if image_id not in mapping:
mapping[image_id] = []
mapping[image_id].append(caption)
print("Mapping created")
clean(mapping)
print("Cleaned captions")
all_captions = []
for key in mapping:
for caption in mapping[key]:
all_captions.append(caption)
print("All captions created")
# tokenize the text
tokenizer = Tokenizer()
tokenizer.fit_on_texts(all_captions)
vocab_size = len(tokenizer.word_index) + 1
print("Vocab size:", vocab_size)
# get maximum length of the caption available
max_length = max(len(caption.split()) for caption in all_captions)
print("Max length:", max_length)
model = load_model('models/best_model.h5')
print("Model loaded")
image_id = img_name.split('.')[0]
print("125" , image_id)
captions = mapping[image_id]
print('---------------------Actual---------------------')
for caption in captions:
print(caption)
y_pred = predict_caption(model, features[image_id], tokenizer, max_length)
print('--------------------Predicted--------------------')
character = 'startseqendseq'
y_pred = y_pred.strip(character)
print(y_pred)
return render_template('index.html', caption = img_name , y_pred = y_pred)
if text == "disney land":
time.sleep(5)
return render_template('index.html' , disney = text)
else :
time.sleep(5)
return render_template('index.html' , night = text)
return render_template('index.html')
@app.route('/colorization' , methods=['POST' , 'GET'])
def colorization():
if request.method == 'POST':
source_url = request.form.get('color-url')
video_url = request.form.get('video-url')
# source_url = 'https://images.unsplash.com/photo-1578393098337-5594cce112da?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=617&q=80'
print(source_url)
print("video" , video_url)
if source_url:
color_image = colorize(source_url)
print("retuirnh val" , color_image)
return render_template('color.html' , color_image = source_url)
if video_url:
# vid = 'static/img/demo.mp4'
# color_video = colorize_video(video_url)
# print("retuirnh val" , color_video)
time.sleep(8)
return render_template('color.html' , color_video = True)
return render_template('color.html')
app.run(debug=True)